Sentiment Classification¶
Loading the dataset¶
In [0]:
#Import libraries
%tensorflow_version 1.x
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential, Model
from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D, Dropout, Activation
from sklearn.model_selection import train_test_split
from keras.utils.np_utils import to_categorical
import re
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
In [2]:
# save np.load
np_load_old = np.load
# modify the default parameters of np.load
np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k)
from keras.datasets import imdb
vocab_size = 10000 #vocab size
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=vocab_size)
# vocab_size is no.of words to consider from the dataset, ordering based on frequency.
np.load = np_load_old
Downloading data from https://s3.amazonaws.com/text-datasets/imdb.npz 17465344/17464789 [==============================] - 1s 0us/step 17473536/17464789 [==============================] - 1s 0us/step
In [0]:
from keras.preprocessing.sequence import pad_sequences
vocab_size = 10000 #vocab size
maxlen = 300 #number of word used from each review
Train test split¶
In [0]:
#load dataset as a list of ints
#make all sequences of the same length
X_train = pad_sequences(X_train, maxlen=maxlen)
X_test = pad_sequences(X_test, maxlen=maxlen)
In [5]:
print(X_train)
[[ 0 0 0 ... 19 178 32] [ 0 0 0 ... 16 145 95] [ 0 0 0 ... 7 129 113] ... [ 0 0 0 ... 4 3586 2] [ 0 0 0 ... 12 9 23] [ 0 0 0 ... 204 131 9]]
In [6]:
print(y_train)
[1 0 0 ... 0 1 0]
In [7]:
print('X_train shape:', X_train.shape)
print('X_test shape:', X_test.shape)
print('y_train shape:', y_train.shape)
print('y_test shape:', y_test.shape)
('X_train shape:', (25000, 300))
('X_test shape:', (25000, 300))
('y_train shape:', (25000,))
('y_test shape:', (25000,))
In [8]:
print(X_train[0])
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 1 14
22 16 43 530 973 1622 1385 65 458 4468 66 3941 4 173
36 256 5 25 100 43 838 112 50 670 2 9 35 480
284 5 150 4 172 112 167 2 336 385 39 4 172 4536
1111 17 546 38 13 447 4 192 50 16 6 147 2025 19
14 22 4 1920 4613 469 4 22 71 87 12 16 43 530
38 76 15 13 1247 4 22 17 515 17 12 16 626 18
2 5 62 386 12 8 316 8 106 5 4 2223 5244 16
480 66 3785 33 4 130 12 16 38 619 5 25 124 51
36 135 48 25 1415 33 6 22 12 215 28 77 52 5
14 407 16 82 2 8 4 107 117 5952 15 256 4 2
7 3766 5 723 36 71 43 530 476 26 400 317 46 7
4 2 1029 13 104 88 4 381 15 297 98 32 2071 56
26 141 6 194 7486 18 4 226 22 21 134 476 26 480
5 144 30 5535 18 51 36 28 224 92 25 104 4 226
65 16 38 1334 88 12 16 283 5 16 4472 113 103 32
15 16 5345 19 178 32]
In [9]:
print(y_train[0])
1
In [10]:
print("Maximum value of a word index ")
print(max([max(sequence) for sequence in X_train]))
print("Maximum length num words of review in train ")
print(max([len(sequence) for sequence in X_train]))
Maximum value of a word index 9999 Maximum length num words of review in train 300
In [68]:
sentiment_df = pd.DataFrame(data = y_train)
sentiment_df.columns = ['sentiment']
sentiment_df.head(4)
sentiment_df.replace(to_replace = 0, value = "Negative", inplace=True)
sentiment_df.replace(to_replace = 1, value = "Positive", inplace=True)
sentiment_df.head()
sns.countplot(x='sentiment', data=sentiment_df)
Out[68]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f6da17c0850>
The words are stored in a key-value pair in the IMDB keras dataset. Let's find out the key-value pairs for the word and corresponding word-id.
In [11]:
word_to_id = imdb.get_word_index()
Downloading data from https://s3.amazonaws.com/text-datasets/imdb_word_index.json 1646592/1641221 [==============================] - 1s 0us/step 1654784/1641221 [==============================] - 1s 0us/step
In [12]:
print(word_to_id)
{u'fawn': 34701, u'tsukino': 52006, u'nunnery': 52007, u'sonja': 16816, u'vani': 63951, u'woods': 1408, u'spiders': 16115, u'hanging': 2345, u'woody': 2289, u'trawling': 52008, u"hold's": 52009, u'comically': 11307, u'localized': 40830, u'disobeying': 30568, u"'royale": 52010, u"harpo's": 40831, u'canet': 52011, u'aileen': 19313, u'acurately': 52012, u"diplomat's": 52013, u'rickman': 25242, u'rumbustious': 52014, u'familiarness': 52015, u"spider'": 52016, u'hahahah': 68804, u"wood'": 52017, u'transvestism': 40833, u"hangin'": 34702, u'screaming': 1927, u'seamier': 40834, u'wooded': 34703, u'bravora': 52018, u'grueling': 16817, u'wooden': 1636, u'wednesday': 16818, u"'prix": 52019, u'altagracia': 34704, u'circuitry': 52020, u'crotch': 11585, u'busybody': 57766, u"tart'n'tangy": 52021, u'pantheistic': 52022, u'thrace': 52023, u"tom's": 11038, u'snuggles': 52025, u"frasier's": 52026, u'complainers': 52027, u'templarios': 52125, u'272': 40835, u'273': 52028, u'zaniacs': 52130, u'275': 34706, u'consenting': 27631, u'snuggled': 40836, u'inanimate': 15492, u'uality': 52030, u'bronte': 11926, u'errors': 4010, u'dialogs': 3230, u'hp': 23593, u"yomada's": 52031, u"madman's": 34707, u'dialoge': 30585, u'usenet': 52033, u'videodrome': 40837, u"kid'": 26338, u'pawed': 52034, u"'girlfriend'": 30569, u"'pleasure": 52035, u"'reloaded'": 52036, u"kazakos'": 40839, u'rocque': 52037, u'mailings': 52038, u'brainwashed': 11927, u'mcanally': 16819, u"tom''": 52039, u'kurupt': 25243, u'affiliated': 21905, u'babaganoosh': 52040, u"noe's": 40840, u'quart': 40841, u'kids': 359, u'uplifting': 5034, u'controversy': 7093, u'kida': 21906, u'kidd': 23379, u"error'": 52041, u'neurologist': 52042, u'spotty': 18510, u'cobblers': 30570, u'projection': 9878, u'fastforwarding': 40842, u'sters': 52043, u"eggar's": 52044, u'etherything': 52045, u'airball': 34708, u'unsinkable': 25244, u'stern': 7180, u"cervi's": 52046, u'dnd': 40844, u'dna': 11586, u'insecurity': 20598, u"'reboot'": 52047, u'trelkovsky': 11037, u'jaekel': 52048, u'sidebars': 52049, u"sforza's": 52050, u'distortions': 17633, u"bynes'": 69999, u'sermons': 30602, u'7ft': 40846, u'boobage': 52052, u"o'bannon's": 52053, u'populations': 23380, u'chulak': 52054, u'mesmerize': 27633, u'quinnell': 52055, u'yahoo': 10307, u'peahi': 52056, u'meteorologist': 52057, u'beswick': 42577, u'boorman': 15493, u'ravensbr\xfcck': 51678, u'voicework': 40847, u"ster'": 52058, u'blustering': 22922, u'intake': 27634, u'morally': 5621, u'sky\xa1\xa8': 69580, u'jumbling': 40849, u'bowersock': 52060, u"'porky's'": 52061, u'gershon': 16821, u'ludicrosity': 40850, u'coprophilia': 52062, u'expressively': 40851, u"india's": 19500, u"post's": 34710, u'wana': 52063, u'wang': 5283, u'wand': 30571, u'wane': 25245, u'disparagement': 52064, u'titanium': 34711, u'pinta': 40852, u'want': 178, u'pinto': 30572, u'whoopdedoodles': 52065, u'tchaikovsky': 21908, u'travel': 2103, u"'victory'": 52066, u'copious': 11928, u"chapters'": 52067, u'barbra': 6702, u'uselessness': 30573, u"wan'": 52068, u'assimilated': 27635, u'petiot': 16116, u'dinosaurs': 3930, u'wrong': 352, u'seda': 52070, u'stollen': 52071, u'sentencing': 34712, u'ouroboros': 40853, u'assimilates': 40854, u'colorfully': 40855, u'glenne': 27636, u"'caricature'": 70418, u'subplots': 4760, u'kiloton': 52073, u'chandon': 23381, u"effect'": 34713, u'flawlessness': 88437, u'snugly': 27637, u'kuei': 40856, u'welcomed': 9092, u'dishonor': 30071, u'concurrence': 52075, u'stoicism': 23382, u"beroemd'": 52077, u'butcher': 6703, u"melfi's": 40857, u'aargh': 30623, u'playhouse': 20599, u'wickedly': 11308, u'fit': 1180, u'labratory': 52078, u'lifeline': 40859, u'bringing': 2338, u'fix': 4287, u'cineliterate': 52079, u'fic': 52080, u'fia': 52081, u'fig': 34714, u'fmvs': 52082, u'fie': 52083, u'reentered': 52084, u'fin': 30574, u'doctresses': 52085, u'fil': 52086, u'zucker': 12606, u'ached': 31931, u'counsil': 52088, u'paterfamilias': 52089, u'songwriter': 13885, u'shivam': 34715, u'hurting': 9654, u'effects': 299, u'kid\xf4': 83921, u'slauther': 52090, u"'flame'": 52091, u'broiler': 70420, u'interwhined': 52093, u'whacking': 27638, u'bartok': 52094, u'barton': 8775, u'frewer': 21909, u"fi'": 52095, u'ingrid': 6192, u'stribor': 30575, u'approporiately': 52096, u'wobblyhand': 52097, u'tantalisingly': 52098, u'ankylosaurus': 52099, u'parasites': 17634, u'childen': 52100, u"jenkins'": 52101, u'metafiction': 52102, u'golem': 17635, u'indiscretion': 40860, u"reeves'": 23383, u"inamorata's": 57781, u'brittannica': 52104, u'adapt': 7916, u"russo's": 30576, u'guitarists': 48246, u'abbott': 10553, u'abbots': 40861, u'lanisha': 17649, u'magickal': 40863, u'mattter': 52105, u"'willy": 52106, u'pumpkins': 34716, u'stuntpeople': 52107, u'fi\x85': 84457, u'estimate': 30577, u'ugghhh': 40864, u'gameplay': 11309, u"wern't": 52108, u'rebels\x85': 84863, u"n'sync": 40865, u'sickeningly': 16117, u'chiara': 40866, u'disturbed': 4011, u'portmanteau': 40867, u'ineffectively': 52109, u"duchonvey's": 82143, u"nasty'": 37519, u'purpose': 1285, u'lazers': 52112, u'breed': 5284, u'kaliganj': 52113, u'popularism': 52114, u"damme's": 18511, u'stylistics': 30578, u'mindgaming': 52115, u"1997'": 52116, u"'corny'": 52117, u'boerner': 34718, u'olds': 6792, u'bakelite': 52118, u'renovated': 27639, u'forrester': 27640, u'timid': 12004, u'gaskets': 52024, u'needed': 884, u'smight': 34719, u'master': 1297, u"edie's": 25905, u'seeber': 40868, u'hiya': 52120, u'fuzziness': 52121, u'genesis': 14897, u'rewards': 12607, u'enthrall': 30579, u'nemesis\x85': 54439, u'splendiferous': 53089, u"recollection's": 52122, u'mutilated': 11039, u'fatherlands': 52123, u"fischer's": 52124, u'positively': 5399, u'270': 34705, u'ahmed': 34720, u'zatoichi': 9836, u'miniatures': 20046, u'anniversaries': 52127, u"helm's": 30580, u"'work'": 52128, u'exclaimed': 34721, u"'unfunny'": 52129, u'274': 52029, u'feeling': 544, u"wanda's": 52131, u'dolan': 33266, u'kiernan': 46193, u'278': 52133, u'peacoat': 52134, u'brawny': 40870, u'mishra': 40871, u'worlders': 40872, u'protags': 52135, u'skullcap': 52136, u'pompous': 8288, u'affairs': 5622, u'wholesome': 7799, u'hymen': 52137, u'paramedics': 25246, u'unpersons': 52138, u'heavyarms': 52139, u'affaire': 52140, u'coulisses': 52141, u'hymer': 40873, u'kremlin': 52142, u'shipments': 30581, u'pixilated': 52143, u"'00s": 30582, u'diminishing': 18512, u'cinematic': 1357, u'resonates': 14898, u'simplify': 40874, u'tensely': 70427, u'temptresses': 40876, u'reverence': 16822, u'resonated': 19502, u'dailey': 34722, u'attach\xe9s': 42050, u'treize': 27641, u'majo': 52145, u'kiya': 21910, u'woolnough': 52146, u'thanatos': 39797, u'plays\x85jack': 55347, u'sandoval': 35731, u'dorama': 40879, u"o'shaughnessy": 52147, u'tech': 4988, u'fugitives': 32018, u'teck': 30583, u'purged': 52149, u'saying': 657, u"martians'": 41095, u'norliss': 23418, u'dickey': 27642, u'dicker': 52152, u"'sependipity": 52153, u'padded': 8422, u'ordell': 57792, u"sturges'": 40882, u'independentcritics': 52154, u'tempted': 5745, u"atkinson's": 34724, u'hounded': 25247, u'apace': 52155, u'clicked': 15494, u"'humor'": 30584, u"martino's": 17177, u"'supporting": 52156, u'warmongering': 52032, u"zemeckis's": 34725, u'lube': 21911, u"ernest's": 70431, u'plate': 7476, u'plata': 40883, u'sturgess': 40884, u"nerds'": 40885, u'plato': 20600, u'plath': 34726, u'platt': 40886, u'mcnab': 52159, u'clumsiness': 27643, u'altogether': 3899, u'rugged': 9093, u'bicenntinial': 52160, u'skaal': 40887, u'droning': 14360, u'lds': 8776, u'jaguar': 21912, u"cale's": 34727, u'nicely': 1777, u'patch': 10086, u'kerkhof': 50202, u"leader's": 52161, u'seriously\x85': 49806, u'circling': 19503, u"'movie": 27644, u'uncomfirmed': 52162, u'heirloom': 40888, u"'stargate'": 52164, u'pinoy': 40889, u'conchatta': 40890, u'broeke': 41128, u'advisedly': 40891, u"barker's": 17636, u'descours': 52166, u'lots': 772, u'lotr': 9259, u'irs': 9879, u'lott': 52167, u'xvi': 40892, u'irk': 34728, u'irl': 52168, u'ira': 6887, u'belzer': 21913, u'irc': 52169, u'ire': 27645, u'requisites': 40893, u'discipline': 7693, u'lyoko': 52961, u'extend': 11310, u'nature': 873, u"'dickie'": 52170, u'fruits': 21460, u'lapping': 30586, u'superficial': 3900, u'vestment': 52171, u"cabal's'": 54638, u'extent': 2823, u'tendons': 52172, u"heller's": 52173, u'quagmires': 52174, u'miyako': 52175, u'moocow': 20601, u"coles'": 52176, u'lookit': 40895, u'ravenously': 52177, u'levitating': 40896, u'perfunctorily': 52178, u'lookin': 30587, u'thialnd': 88440, u"lot'": 40898, u'lookie': 52179, u'fearlessly': 34870, u'libyan': 52181, u'fondles': 40899, u'gopher': 35714, u'wearying': 40901, u"nz's": 52182, u'minuses': 27646, u'puposelessly': 52183, u'shandling': 52184, u'surname': 23384, u'humming': 11929, u"'nother": 40902, u'smackdown': 21914, u'underdone': 30588, u'frf': 40903, u'triviality': 52185, u'fro': 25248, u'bothers': 8777, u"'kensington": 52186, u'much': 73, u'muco': 34730, u'chink': 27647, u"richie's": 27648, u'tonino': 40904, u'unleavened': 52187, u'fry': 11587, u"'tv'": 40905, u'toning': 40906, u'obese': 14361, u'sensationalized': 30589, u'spiv': 40907, u'spit': 6259, u'fidgetting': 70437, u'arkin': 7364, u'charleton': 21915, u'jeon': 16823, u'boardroom': 21916, u'doubts': 4989, u'spin': 3084, u'skilfully': 30590, u'wildcat': 27649, u'unecessary': 52189, u'misconstrues': 52191, u'mesmerising': 18514, u'misconstrued': 40908, u'rescinds': 52192, u'prostrate': 52193, u'belittling': 47367, u'majid': 40909, u"shock'": 52194, u'canoeing': 34731, u'majin': 52195, u"berkeley's": 21917, u'sylke': 40910, u'conditioned': 14899, u'waddell': 40911, u'hyperdrive': 41188, u'conditioner': 34732, u'hone': 30591, u'hong': 2576, u'memoriam': 52198, u'inventively': 30592, u"levant's": 25249, u'portobello': 20638, u'remand': 52200, u'mummified': 19504, u'honk': 27650, u'spews': 19505, u'visitations': 40912, u'mummifies': 52201, u'cavanaugh': 25250, u'zeon': 23385, u"jungle's": 40913, u'chauvinistic': 19544, u'frenchmen': 27651, u'torpedoes': 52202, u'schlessinger': 52203, u'torpedoed': 34734, u'blister': 69876, u'cinefest': 52204, u'furlough': 34735, u'mainsequence': 52205, u'mentors': 40914, u'academic': 9094, u'stillness': 20602, u'academia': 40915, u'lonelier': 52206, u'nibby': 52207, u"losers'": 52208, u'cineastes': 40916, u'corporate': 4449, u'massaging': 40917, u'bellow': 30593, u'absurdities': 19506, u'expetations': 53241, u'nyfiken': 40918, u'lasse': 52209, u'visability': 52210, u"elder'": 52211, u'gainsbourg': 19023, u'hah': 20603, u'hai': 13420, u'upsides': 53255, u'hak': 25251, u'hal': 4311, u'ham': 4892, u'han': 8058, u'haa': 52213, u'had': 66, u'advancement': 11930, u'hag': 16825, u"hand'": 25252, u'hay': 13421, u'mcnamara': 20604, u"mozart's": 52214, u'hap': 25253, u'haq': 30594, u'har': 13887, u'has': 44, u'hat': 2401, u'hav': 40919, u'haw': 30595, u'cocked': 30596, u'municipal': 34927, u'elders': 15495, u'underpanted': 52216, u'pninson': 52217, u'unequivocally': 27652, u'palazzo': 52218, u"bello'": 52219, u'indicative': 12997, u'yawnfest': 40920, u'hexploitation': 52220, u"loder's": 52221, u'sleuthing': 27653, u'darkon': 40921, u"'ball": 52222, u"'summer": 52223, u"'demons'": 34935, u"mormon's": 52225, u"laughton's": 34737, u'debell': 52226, u'shipyard': 39724, u'unabashedly': 30597, u'disks': 40401, u'crowd': 2290, u'crowe': 10087, u"vancouver's": 56434, u'mosques': 34738, u'crown': 6627, u'culpas': 52227, u'captive': 8428, u'surrell': 53344, u'flowless': 52229, u'sheirk': 52230, u"'three": 40923, u"peterson'": 52231, u'ooverall': 52232, u'perchance': 40924, u'bottom': 1321, u'chabert': 53363, u'sneha': 52233, u'inhuman': 13888, u'ichii': 52234, u'ursla': 52235, u'completly': 30598, u'moviedom': 40925, u'raddick': 52236, u'brigades': 40926, u'stutter': 25254, u'starring': 1181, u"'goal'": 52237, u'caskets': 52238, u'willcock': 52239, u"threesome's": 52240, u"mosque'": 52241, u"cover's": 52242, u'spaceships': 17637, u'anomalous': 40927, u'ptsd': 27655, u'shirdan': 52243, u'gra\xe7a': 51916, u'lemmings': 30599, u'duccio': 30600, u"levene's": 52244, u"'gorby'": 52245, u"teenager's": 25255, u'marshall': 5340, u'honeymoon': 9095, u'shoots': 3231, u'despised': 12258, u'okabasho': 52246, u'fabric': 8289, u'cannavale': 18515, u'raped': 3537, u"tutt's": 52247, u'grasping': 17638, u'despises': 18516, u"thief's": 40928, u'rapes': 8926, u'raper': 52248, u"eyre'": 27656, u'walchek': 52249, u"elmo's": 23386, u'perfumes': 40929, u'spurting': 21918, u'denoting': 52251, u'thesaurus': 34740, u"shoot'": 40930, u'bonejack': 49759, u'simpsonian': 52253, u'hebetude': 30601, u"hallow's": 34741, u'incinerator': 34742, u'congratulations': 10308, u'humbled': 52255, u"else's": 5924, u'trelkovski': 40845, u"rape'": 52256, u'1600s': 52257, u'martian': 7253, u'nicest': 25256, u'eyred': 52259, u'passenger': 9457, u'disgrace': 6041, u'moderne': 52260, u'barrymore': 5120, u'yankovich': 52261, u'moderns': 40931, u'studliest': 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u'cleverless': 84561, u'pitying': 19492, u'woodsmen': 40572, u'bentley': 16111, u'pallance': 63915, u'ponderance': 88387, u'bhand': 54048, u'apophis': 25226, u'fusing': 51934, u'bhang': 88388, u'celticism': 88389, u'achile': 88390, u'mstifyed': 73285, u"breathnach's": 88392, u'stanly': 87351, u'pam': 9091, u'siani': 51935, u'phoebus': 40791, u'flapjack': 51936, u'category': 2382, u'puertorican': 30555, u"'best": 38663, u'dispossessed': 34678, u"montand's": 51937, u'garcia': 9628, u"franko's": 88395, u'outings': 12604, u'obstructions': 51938, u'dominion': 23173, u"daria's": 51939, u'grits': 40792, u'poets': 16812, u'brilliantly': 2102, u'riddick': 66387, u'swings\x97but': 88067, u'dictionary': 14355, u'nuit': 23373, u"ashram's": 88396, u'abductor': 34679, u'displeasing': 88397, u'obliteration': 88398, u'by\x85': 42358, u'bedazzled': 51941, u'kober': 19493, u'rackaroll': 87424, u'cait': 88400, u"outing'": 88401, u'segregated': 34680, u'substantiate': 88403, u'reexamined': 88404, u'horace': 25227, u"'oldest": 88405, u'brundage': 51995, u'inseglet': 88406, u'caio': 51943, u'cain': 7914, u'californian': 31925, u"critique's": 88407, u'timbers': 40793, u'firehouse': 25229, u"wenders'": 34681, u'jarrell': 40794, u'afgan': 88408, u'bleakness': 25230, u'homere': 88409, u'treaters': 51944, u"garner's": 31001, u'185': 51945, u'gratuitously': 25231, u'l\xe8vres': 31443, u'harmlessness': 88411, u'probation': 13867, u'homers': 88413, u'garard': 88414, u'destinations': 27621, u'flourish': 16813, u'myspace': 25232, u'accent': 1188, u'weberian': 88415, u'drood': 88416, u'drool': 14356, u'darma': 88417, u'thread': 5554, u'elbowroom': 88418, u'peoria': 40795, u'kiera': 16112, u'resurrects': 25233, u"sheep'": 50039, u'wanted': 470, u'constituent': 88419, u'evisceration': 40797, u"flik's": 40798, u'osenniy': 88420, u'scryeeee': 51946, u'nationalistic': 76077, u'swooned': 51948, u'tommy': 4041, u'morely': 88421, u'leticia': 51949, u'harald': 40106, u'sculpture': 17626, u'bodice': 51950, u'beter': 88422, u"1992's": 34683, u'outsmarted': 27206, u'clevage': 88423, u'wathced': 88424, u'woebegone': 40799, u'alfonso': 17611, u'pealed': 88426, u'alfonse': 51951, u'florin': 88427, u'forthcoming': 13881, u'florid': 51952, u"'gregory's": 63920, u'oozing': 17627, u'agency': 5744, u'zhukov': 51953, u'divorc\xe9es': 74220, u'desando': 88429, u'maharashtrian': 88430, u'spawns': 26664, u'stying': 88431, u"den'": 88432, u'divers': 18506, u"theme'": 57589, u'salkow': 40800, u'adamson': 18338, u'centre': 5923, u"'dim'": 71522, u"brynhild's": 73678, u'doyleluver': 88436, u'1881': 39608, u'slitting': 40162, u"eustache's": 30556, u'deny': 6107, u"mcelwee's": 40801, u"quiroz's": 88438, u'jpieczanski': 88439, u'belphegor': 78164, u'dens': 88441, u'syncretism': 88442, u"oozin'": 87659, u'goodhearted': 51954, u'dent': 23374, u'xavier': 25234, u'dena': 88444, u'inane': 4482, u'mckellar': 88445, u"word'": 51712, u'nailgun': 87692, u'myrna': 7798, u'illuminations': 31264, u'camion': 88447, u'waddlesworth': 51955, u'laughers': 88448, u'playbook': 88449, u'powerhouse': 12996, u'500db': 88450, u'holistic': 40804, u'foreclosure': 57743, u'disgracing': 87713, u'asterix': 88452, u"generation'": 51956, u'fans': 448, u'humanitarian': 40805, u'candidates': 9455, u'lassiter': 51957, u"troops'": 78083, u'thousands': 3083, u'forfeit': 88454, u'philipps': 20126, u'merchandises': 52510, u'shlocky': 51958, u'herinteractive': 69930, u'goosebumps': 21897, u"skogland's": 51959, u'winsor': 40806, u'998': 88457, u'woooooo': 88458, u'bulimics': 47980, u'bluray': 51960, u"maker's": 20575, u'wordy': 17628, u'swindle': 51961, u"candidate'": 88459, u'mulford': 88460, u'sappily': 88461, u'waterfalls': 34685, u'jansch': 88462, u'asian': 2185, u'generations': 5119, u'ebb': 30558, u"astronaut's": 87813, u'seeded': 50042, u'detatched': 88465, u'paulson': 30559, u"'80ies": 88466, u'violet': 14357, u'gutters': 88467, u'scoured': 88468, u'etchings': 51962, u"'japonese": 82107, u'word\x85': 83264, u'closes': 11036, u"god\x85yes'": 62088, u'closet': 4250, u'magus': 88471, u'eggleston': 88472, u'genius': 1259, u'ashleigh': 34686, u'rumblings': 51963, u'panty': 40807, u'closed': 4587, u'dividends': 51964, u'simms': 27624, u'brosnan': 3974, u'linebacker': 51965, u'complement': 10552, u'pants': 4515, u'addio': 88474, u'99\xbd': 65929, u'audiovisual': 51966, u'beverages': 21898, u'brigadoon': 17629, u'salesgirl': 40808, u'99\xa2': 63417, u'doesnot': 88475, u'sexploitation': 13882, u"jame's": 88476, u'memento': 19494, u'voight': 4249, u'soapbox': 30560, u'famke': 40809, u'profanity': 5398, u'broadside': 40713, u'prefer': 2785, u"'30's": 18972, u"concert'": 88480, u'clansmen': 34687, u'agito': 51968, u'safely': 7475, u'kaempfen': 88482, u'withdraws': 30513, u'invetigator': 88483, u'sleaziest': 34688, u'guerin': 88484, u'inflicted': 9876, u'neighborliness': 88485, u'withdrawn': 16113, u'withdrawl': 88486, u'bbm': 87940, u'irreverence': 22524, u'klara': 23359, u'bbe': 88489, u'bbc': 2983, u'immortality': 11357, u'validating': 51969, u'suffocate': 51970, u'jugars': 88491, u'mambo\x85': 81264, u'impetuously': 88492, u'thumbes': 88493, u'bbs': 51971, u'bbq': 34690, u'butchers': 23375, u'verizon': 88494, u'butchery': 40810, u"genie's": 88495, u'meaningless': 4042, u'saner': 88496, u"'prestige'": 51972, u'revivalist': 48235, u"carface's": 40811, u"'deepness'": 88497, u"agreement'": 88498, u'aiieeee': 88499, u"sons'": 39208, u'irate': 27588, u'intresting': 80934, u'readies': 76453, u'nilamben': 88503, u'sebastien': 34692, u'funnist': 88504, u'marshmorton': 39736, u'melo': 49212, u"marcel's": 51973, u'restructure': 88505, u'madrasi': 88506, u'piteously': 81288, u'garda': 40812, u"biggie's": 40813, u'insecurities\x85': 69061, u'gassing': 40814, u'stadiums': 34693, u'vintage': 6626, u'loutishness': 51975, u'headlight': 18507, u'nwhere': 88509, u'adverse': 19495, u'merciful': 51976, u'panning': 20594, u'keneth': 40815, u'fomentation': 88510, u'hopton': 88511, u"sousa's": 51977, u'tactful': 51978, u'booz': 88512, u'craigs': 88513, u'transacting': 88577, u'brumes': 88514, u'snatchers': 16814, u"'cartoons'": 51979, u'baruc': 88515, u'albin': 88516, u"'masters": 71927, u'antipasto': 88517, u'overwork': 88518, u'appropriates': 88519, u'equivalencing': 76094, u'outgrow': 40816, u'breaker': 17630, u'boor': 51981, u'contenders': 27601, u'rocker': 9877, u"arcand's": 88522, u'gleason\x85': 74296, u'rocket': 4347, u'wren': 88523, u'lowlights': 51982, u'cloaks': 88139, u'mighty': 4884, u'hmapk': 88525, u'rocked': 11939, u'downfalls': 34694, u'shigeru': 35318, u'disturbance': 19496, u'serbs': 12605, u'ensued': 22958, u'sky': 1746, u'morbidly': 21900, u'googling': 51983, u"'intentional'": 82114, u'clyde': 8286, u'ska': 51984, u'timecrimes': 88528, u'serbo': 88529, u'ski': 16114, u'knob': 30562, u'saturation': 25238, u"andr\xe9's": 34260, u'branch': 9456, u'refusals': 54834, u'protesters': 21901, u'kristofferson': 8774, u'siemens': 51985, u'deviances': 53304, u'know': 121, u'knot': 30563, u'knox': 9311, u'\xc5ge': 59844, u'renyolds': 40817, u'iberia': 17631, u"annabelle's": 88531, u'bedfellows': 40818, u'praarthana': 88532, u'losted': 88533, u'cloistering': 70961, u'dances': 4200, u"one''willard'": 88534, u'servant': 5382, u'isolytic': 88536, u'starred': 2681, u'traitors': 27626, u'amores': 25239, u'shabby': 12257, u'sequence': 717, u"matrix'": 23376, u'birthmark': 51986, u'mosquitoes': 34696, u'libretto': 34697, u'kattan': 13418, u'holler': 34664, u'soninha': 34040, u'nilsen': 88287, u'dubbers': 88539, u'mancoy': 55997, u"gettin'": 51988, u'zanuck': 17632, u'leaded': 51989, u'strolls': 29326, u'moguls': 40820, u'catepillar': 88540, u'sette': 40821, u'leader': 2118, u'murdock': 23377, u'uneffective': 88541, u'haywood': 51990, u'miniskirts': 88542, u'pensaba': 88543, u'thoroughfare': 88544, u"o'donoghugh": 51991, u'arganauts': 76917, u'swinginest': 88545, u'hitchhiking': 30548, u'wesley': 7915, u'daud': 88546, u'guffaws': 30564, u'empahh': 88547, u'nationalities': 30565, u'throne': 8594, u'intercontenital': 88548, u'throng': 51992, u'getting': 394, u'klineschloss': 51993, u'dependence': 21902, u'dependency': 25240, u'epiphany': 19497, u'fudd': 25241, u'wonk': 76097, u'fitzgibbon': 88549, u'bugle': 27627, u'unoticeable': 88550, u'couldnt': 24879, u'gunther': 30566, u'cultureless': 57756, u"'hall": 88551, u"'dollman'": 88386, u'registration': 21903, u'madhavi': 30567, u'fired': 3426, u'patronisingly': 51994, u'maize': 88554, u'uncontrollably': 21904, u'expeditious': 88555, u'noxious': 51942, u'hypnotism': 27628, u"'half": 88556, u'lederer': 88557, u'uncontrollable': 14359, u'footwork': 34698, u"i've": 204, u'proving': 5974, u'worriedly': 42569, u'baaaaad': 40824, u'gereco': 44477, u"bearings'": 88558, u'kensit': 51997, u'wight': 88559, u'brashness': 34699, u'bratwurst': 51998, u"may's": 14128, u'contradictors': 88560, u'amitabhs': 88561, u'jaffa': 19498, u'jaffe': 11924, u'instructive': 40826, u"olan's": 88562, u'ornella': 51999, u'bitva': 57759, u'reble': 88564, u'percival': 88565, u'lubricated': 88566, u"matsumoto's": 34700, u'heralding': 88567, u'hirschbiegel': 52001, u"baywatch'": 88568, u'odilon': 88569, u'vans': 18508, u'gnawing': 40827, u"markham's": 63863, u"guard's": 88571, u"nemesis'": 88572, u'spookfest': 57761, u"'transporter'": 54907, u'airsoft': 88573, u'abhay': 11306, u'spanky': 23378, u'ev': 88575, u'chicatillo': 88576, u"dance'": 34064, u"'la": 27630, u'percent': 8925, u'oprah': 7996, u'sics': 88578, u'illinois': 11925, u'dogtown': 40828, u'roars': 20595, u'sick': 1192, u'kerouac': 52002, u'wheelers': 88579, u'sica': 20596, u'lance': 6435, u"pipe's": 88580, u'discretionary': 64179, u'contends': 40829, u'copywrite': 88581, u'geysers': 52003, u'artbox': 88582, u'cl\xe9ment': 51107, u'cronyn': 52004, u'hardboiled': 52005, u"voorhees'": 88583, u'35mm': 16815, u"'l'": 88584, u'urrrghhh': 88574, u'expands': 20597}
In [13]:
INDEX_FROM = 3
word_to_id = imdb.get_word_index()
word_to_id = {k:(v+INDEX_FROM) for k,v in word_to_id.items()}
word_to_id["<PAD>"] = 0
word_to_id["<START>"] = 1
word_to_id["<UNK>"] = 2
word_to_id["<UNUSED>"] = 3
id_to_word = {value:key for key,value in word_to_id.items()}
print(' '.join(id_to_word[id] for id in X_train[0] ))
<PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <START> this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert <UNK> is an amazing actor and now the same being director <UNK> father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for <UNK> and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also <UNK> to the two little boy's that played the <UNK> of norman and paul they were just brilliant children are often left out of the <UNK> list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all
In [14]:
print(word_to_id.keys())
[u'fawn', u'tsukino', u'nunnery', u'sonja', u'vani', u'woods', u'spiders', u'hanging', u'woody', u'trawling', u"hold's", u'comically', u'localized', u'disobeying', u"'royale", u"harpo's", u'canet', u'aileen', u'acurately', u"diplomat's", u'rickman', u'rumbustious', u'familiarness', u"spider'", u'hahahah', u"wood'", u'transvestism', u"hangin'", u'bringing', u'seamier', u'wooded', u'kid\xf4', u'grueling', u'wooden', u'wednesday', u"'prix", u'altagracia', u'circuitry', u'crotch', u'busybody', u"tart'n'tangy", u'burgade', u'thrace', u"tom's", u'snuggles', u'francesco', u'complainers', u'templarios', u'272', u'273', u'zaniacs', u'275', u'consenting', u'snuggled', u'inanimate', u'uality', u'bronte', u'errors', u'dialogs', u'hp', u"yomada's", u"madman's", u'dialoge', u'usenet', u'videodrome', u"kid'", u'pawed', u"'girlfriend'", u"'pleasure", u"'reloaded'", u"kazakos'", u'rocque', u'mailings', u'brainwashed', u'mcanally', u"tom''", u'kurupt', u'affiliated', u'babaganoosh', u"noe's", u'quart', u'kids', u'uplifting', u'controversy', u'kida', u'kidd', u"error'", u'neurologist', u'spotty', u'cobblers', u'projection', u'fastforwarding', u'sters', u"eggar's", u'etherything', u'airball', u'unsinkable', u'stern', u"cervi's", u'dnd', u'dna', u'insecurity', u"'reboot'", u'trelkovsky', u'jaekel', u'sidebars', u"sforza's", u'distortions', u'mutinies', u'sermons', u'7ft', u'boobage', u"o'bannon's", u'populations', u'chulak', u'mesmerize', u'quinnell', u'yahoo', u'meteorologist', u'beswick', u'boorman', u'ravensbr\xfcck', u'voicework', u"ster'", u'blustering', u'hj', u'intake', u'morally', u'sky\xa1\xa8', u'jumbling', u'bowersock', u"'porky's'", u'gershon', u'ludicrosity', u'coprophilia', u'expressively', u"india's", u"post's", u'wana', u'wang', u'wand', u'wane', u'edgeways', u'titanium', u'pinta', u'want', u'pinto', u'whoopdedoodles', u'tchaikovsky', u'travel', u"'victory'", u'copious', u"chapters'", u'barbra', u'uselessness', u"wan'", u'assimilated', u'petiot', u'dinosaurs', u'wrong', 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u'decadent', u'gale', u'haroun', u'alike', u'cleaned', u'gall', u'season3', u'ironsides', u'spurrier', u'tindersticks', u'abromowitz', u'para', u'stupifying', u'sherri', u'gutwrenching', u'cropped', u"direction'", u"'penelope'", u'silent', u'cropper', u'collora', u'dionne', u'gains', u'unperturbed', u'majorca', u'rastin', u'monicas', u'fresco', u'entrenchments', u"bonhoeffer's", u'coastal', u'flemming', u'seasons', u'machete', u'splat', u'menschentr\xfcmmer', u"empire's", u'1040', u'gadsden', u'sensing', u'garofalo', u'sartre', u'wichita', u'artemis', u'hyena', u"maughan's", u'hornblower', u'contenders', u'scaffold', u'judaism', u'kosovo', u'fong', u"o'conner", u'fond', u'gladaitor', u'fonz', u"o'connel", u"'okay'", u'font', u'nuevo', u'underpasses', u'believe', u'rocket', u"carter's", u'sunnybrook', u'memorializing', u'technologically', u'betray', u'blablabla', u'woooooow', u'sacking', u'lovecraftian', u'cloaks', u"iberia's", u'18a', u'thuggee', u'britsh', u'instinctive', u'borje', u"encounter'", u"warhol's'", u'fcked', u'rinaldo', u'rachels', u'cartoons', u'rinaldi', u'scorpiolina', u"miraglio's", u'cartoony', u'arp', u'pvr', u'art', u"archeologist's", u'dump', u'collateral', u"toho's", u'absurdness', u"clive'", u'pvc', u'arc', u'dumb', u'are', u'arg', u'cleverly', u'explosion', u"'director's", u'ark', u'arm', u'aro', u'misfitted', u'fcker', u'haberland', u'gravestones', u'freshmen', u'formatted', u'drooping', u'zelig', u'yakusyo', u'lunceford', u'editorializing', u'plywood', u'banalities', u'nestor', u'revitalizes', u'voguing', u'sedate', u'dictum', u'brasher', u'york', u'unchallengeable', u'subtelly', u'opposition', u'fetchingly', u"'secrets", u'teleflick', u'viennese', u'orphanage', u'movers', u"cameraman's", u"cameraman't", u'pornoes', u'embodiments', u'heorine', u'fraternity', u"'procedures'", u'finds', u'caratherisic', u'munshi', u'clashing', u"mjh's", u'nikah', u'incandescent', u'stowing', u'acrid', u'eyewitness', u'maniacally', u'suspenders', u'acupat', u'nominee', u'toshiro', u"'anita", u'ciannelli', u'clyde', u'posher', u'johannes', u'predeccesors', u'watchword', u'change', u'talkshow', u'ska', u"'colorful'", u'suffocate', u"dial's", u"'notorious'", u'slideshow', u'americaness', u'1861', u'1860', u'1863', u'1862', u'1865', u'1864', u'misbehaving', u"'plague'", u'heighten', u'toppling', u'appallonia', u'fernandel', u'tenuta', u'civility', u'mikaele', u"house's", u"cahiil's", u'abos', u"parents'", u'fernandes', u"ouedraogo's", u'boromir', u'moustache', u"boothe's", u'fernandez', u"star's", u"hoover's", u'riz', u'categorized', u'gastronomic', u'flitted', u'rip', u'rin', u'rio', u'ril', u'rim', u"movin'", u'rif', u'rig', u'rid', u'rib', u'ric', u'ethnicity', u'blackwood', u"what'", u'ignacio', u'lengthy', u'yidische', u'eames', u'lengths', u'bacula', u"'certain", u'ideologies', u'propping', u'chicory', u'hester', u'apeal', u'minis', u'nutjobs', u'novelizations', u'devgn', u'targetting', u'brooding', u'moving', u'incapacitated', u'uneasily', u'obit', u'noodle', u'castigates', u"shame's", u'solemnity', u'antoniette', u'limbaugh', u'abrasive', u'analysis', u'solids', u'castigated', u'broods', u'starved', u'huggie', u"'rangi", u'silvestres', u'bankrolls', u'reincarnates', u'orientalism', u'trickle', u'mysteriosity', u'bancroft', u'reincarnated', u'orientalist', u'inference', u'cabaret', u"santos's", u'gameel', u'\x96a', u"stubby's", u'dabbie', u'navigator', u'violins', u'thrillingly', u'frightner', u'violations', u'essanay', u'devourer', u'joyless', u'beals', u'unlimited', u"matsujun's", u'kass', u'helfgotts', u'kasi', u'kase', u'encounter', u"felt'", u'woefull', u'glittery', u'pithy', u'cameroon', u'glitters', u'incidental', u'italians', u'breeder', u'splatterfest', u'stefanovic', u'insector', u'resourcefully', u'impassioned', u"keepin'", u'burgundians', u'eluding', u'traits', u'marmalade', u'hunziker', u'highschoolers', u"'side", u'ribisi', u'indictable', u'anny', u'pros', u"merry's", u'prop', u'anno', u'prom', u"bogayevicz's", u'necrophilia', u'prof', u'andronicus', u'prod', u'prob', u'apathetic', u'tilts', u'exup\xe9ry', u'swatman', u"carmen'", u'mosh', u'yamasaki', u'wooofff', u'gollam', u'weasels', u'jetson', u'jetsom', u'leonel', u'intense', u"'anastasia", u"glenda's", u'mungle', u"j's", u'seeped', u'tortuous', u'littauer', u'unimpressed', u"'oldie'", u"'toothbrush'", u'credible', u'cutoff', u"montage's", u'incoherence', u"'waster'", u'hatcheck', u'because', u'piteous', u'incoherency', u'credibly', u'colorize', u'slavoj', u"brat's", u'ioffer', u'pallio', u'sndtrk', u"steven's", u'pallid', u'signifies', u'mindbogglingly', u'unadorned', u"beagle's", u'stupefaction', u'signified', u"already's", u"'humans", u"laughed'", u'fabio', u'perplexing', u'conspicious', u'skewers', u'trussed', u'embarasses', u'birthmark', u'orgue', u'crank', u'siodmak', u'familia', u'upright', u'embarassed', u'crane', u'billed', u'115', u"grierson's", u'soutendjik', u'astounds', u"'human'", u'caller', u'matador', u'torpedoing', u"'plan", u"arkin's", u"avigdor's", u'holler', u'obsolescence', u'called', u'110', u'gerda', u'samaurai', u'gerde', u'soars', u"philadelphia'", u'entrance', u"board's", u'ether', u'actores', u"jawab'", u'anamorphic', u'haney', u'nilsen', u"hay's", u'tentative', u'connoisseurship', u'wg', u'supblot', u'bailsman', u"turaquistan's", u'redeaming', u'leaden', u'primetime', u'understatement', u'amrapurkars', u'sheryll', u'92nd', u'inflame', u'pilgrims', u'everone', u'dimeco', u'dead\x97only', u'appalingly', u'delineation', u"2004's", u"shining'", u'we', u'reproaches', u'thames', u'diamiter', u'moveis', u'mardi', u"wells'", u'850pm', u'sacrilage', u'redlitch', u'sadistically', u'lean', u'jacqualine', u'torin', u'disey', u'contributions', u'magnifying', u'cupboards', u"bedroom'", u'showthread', u'someincredibly', u'whig', u'roshan', u'celie', u"'hot'", u'pathologize', u'visualizing', u'hinduism', u'trasforms', u'fences', u'fencer', u'snipes', u'sniper', u'abode', u"'caprica'", u"'moviefreak'", u'lynch', u'bedrooms', u'713', u'honost', u'slickest', u'shuriikens', u'limbs', u'spectaculars', u'harlot', u'harlow', u"o'd", u'notes', u'underscores', u'leader', u'minimizing', u'outdo', u'underscored', u'comlex', u'noted', u'untraditional', u'procession', u'bodybuilding', u"y'know", u"serat's", u'rrw', u'manipulating', u'fermenting', u'cronicles', u'stth', u'palminteri', u'page2', u"foywonder's", u"1934's", u'slur', u'waiting', u'relocate', u'ammunition', u"o'laughlin", u'nightstick', u'flavoured', u"bowden's", u'handedly', u'highwater', u"busted's", u'tarred', u"imposter's", u'insouciance', u'cyclist', u"lestrade's", u'metro', u'spiced', u'hulled', u'microman', u'parroting', u'bickers', u"'dog", u'simpleton', u"'devil'", u'breeds', u"'don", u'transportive', u'appearently', u'apple', u'deputy', u'dwarves', u'overbaked', u'offends', u'herzog', u'ghim', u'cataclysms', u'motor', u'coreys', u'apply', u'chandelere', u'rumiko', u'iced', u'baltimorean', u'marseille', u'enging', u'reanimating', u'slug', u'ices', u'metzger', u'weeping', u'december', u"sun's", u"'starred'", u'briers', u'epiphanal', u'porch', u'credulous', u'philanderer', u"'austin", u'cooperate', u"ruby's", u"'talky'", u'avtar', u'faultless', u'echos', u'penis', u'annoy', u"ice'", u'slaps', u"cgi'd", u'foggiest', u'matelot', u'condominium', u'demonically', u'finacier', u'thepace', u'biscuit', u'proog', u'proof', u'tat', u'tau', u'arlen', u'tap', u'tar', u'yardsale', u'earnestness', u'matinee', u'dummee', u'tax', u'tay', u'taz', u'germanish', u'tad', u'kyer', u'g\xf6ta', u'tag', u'condescend', u'shipping', u'perplexities', u'tac', u'tal', u'tam', u'tan', u'surge', u'onions', u'tai', u'taj', u'tak', u'twizzlers', u"'tinker", u'inaugural', u'fortunate', u'japs', u'actualities', u'testators', u'japp', u"bury's", u'taylors', u'panic', u'inflated', u"'drama", u'perjury', u'sightseeing', u'footpath', u'skullduggery', u"blaine's", u'gaijin', u'fortinbras', u'crawling', u'richthofen', u'elie', u'pah', u'elia', u'cumpsty', u'elio', u'unjustified', u'trophies', u'scoggins', u"'machinal'", u'archiological', u'encyclicals', u'gunslinger', u'meir', u'reeve', u'voiceless', u'byw', u'terriers', u'byu', u'antonius', u'merchandise', u'bye', u'jukebox', u'violating', u'crash', u'yazaki', u'commended', u'knights', u"caliban's", u'commender', u"melbourne's", u'183', u'crass', u'cauffiel', u'transmitter', u'pocus', u'whiteclad', u'sinnister', u'standoffish', u'puya', u'tram', u'eased', u'fiddle', u'brontosaurus', u"by'", u'180', u'tray', u'upheavals', u'cleverless', u'pitying', u'woodsmen', u"'dollman'", u'bentley', u'pallance', u'ponderance', u'registration', u'eloquent', u'apophis', u'fusing', u'bhang', u'celticism', u'achile', u'siana', u"breathnach's", u"'masked", u'pam', u'siani', u'phoebus', u'flapjack', u"'smallpox", u'cosimo', u"'best", u"montand's", u'garcia', u"franko's", u'outings', u'obstructions', u'dominion', u"daria's", u'grits', u'slahsers', u'riddick', u'swings\x97but', u'dictionary', u'nuit', u"ashram's", u'abductor', u'displeasing', u'obliteration', u'by\x85', u'bedazzled', u'kober', u'dormitories', u'cait', u"outing'", u'segregated', u'substantiate', u'reexamined', u'horace', u"'oldest", u'grapevine', u'inseglet', u'caio', u'cain', u'californian', u"critique's", u"munk's", u'firehouse', u"wenders'", u'jarrell', u'afgan', u'bleakness', u'homere', u'treaters', u'hitherto', u'185', u'olde', u'l\xe8vres', u'harmlessness', u'vampress', u'homers', u'garard', u'destinations', u'flourish', u'myspace', u'accent', u'weberian', u'drood', u'drool', u'darma', u'thread', u'elbowroom', u'peoria', u'kiera', u'resurrects', u'greenlight', u'wanted', u'constituent', u'evisceration', u"flik's", u'osenniy', u'kiernan', u'nationalistic', u'swooned', u'tommy', u'morely', u'leticia', u'harald', u'sculpture', u'bodice', u'beter', u'franfreako', u'outsmarted', u'clevage', u'wathced', u'woebegone', u'mongolian', u'pealed', u'donavon', u'florin', u'excusable', u'florid', u'allergies', u'oozing', u'agency', u'zhukov', u'divorc\xe9es', u'maharashtrian', u'divert', u'stying', u"den'", u'divers', u'youuuu', u'\u2018dr', u"feud's", u'centre', u'flesheaters', u'noggin', u'doyleluver', u'flawlessness', u'slitting', u"eustache's", u'deny', u"mcelwee's", u"quiroz's", u'jpieczanski', u'thialnd', u'dens', u'syncretism', u'gingernuts', u'goodhearted', u'dent', u'xavier', u'dena', u'inane', u'mckellar', u'houswives', u'faded', u'myrna', u'illuminations', u'camion', u'waddlesworth', u'laughers', u'playbook', u'powerhouse', u'500db', u"waterfall'", u'foreclosure', u'hamburglar', u'asterix', u"generation'", u'fans', u'operational', u'candidates', u'lassiter', u'cordobes', u'thousands', u'forfeit', u'philipps', u'vacuousness', u'shlocky', u'workin', u'dope', u"skogland's", u'999', u'998', u'woooooo', u'gleeson', u'argento', u'penetrate', u'wordy', u'ghetto', u"candidate'", u'mulford', u'sappily', u'eccentrically', u'jansch', u'asian', u'generations', u'ebb', u"lifeforce'", u'chrstmas', u'detatched', u'paulson', u'churchman', u'violet', u'gutters', u'scoured', u'etchings', u"kylie's", u'closer', u'closes', u'panto', u'closet', u'magus', u'eggleston', u'genius', u'ashleigh', u'rumblings', u'panty', u'closed', u'dividends', u'simms', u'brosnan', u'linebacker', u'complement', u"baddies'", u'addio', u'99\xbd', u'audiovisual', u'beverages', u'brigadoon', u'salesgirl', u'99\xa2', u'doesnot', u'sexploitation', u"jame's", u'memento', u'barrett', u'soapbox', u'famke', u'profanity', u'b36s', u'windom', u"close'", u"concert'", u'clansmen', u'agito', u'wnbq', u'kaempfen', u"thaw's", u'\xa1\xa8', u'sleaziest', u'guerin', u'inflicted', u'neighborliness', u'withdrawn', u'withdrawl', u'freedmen', u"'balkanized'", u'kenobi', u'bbe', u'thumbed', u'window', u'validating', u"boxers'", u'jugars', u'agreements', u'impetuously', u'thumbes', u'bbs', u'bbq', u'butchers', u'verizon', u'butchery', u"genie's", u'saner', u"'prestige'", u'revivalist', u"carface's", u"'deepness'", u"agreement'", u'aiieeee', u'maciste', u'hounsou', u'intresting', u'readies', u'nilamben', u'sebastien', u'funnist', u'marshmorton', u'hobbyhorse', u"marcel's", u'restructure', u'madrasi', u'agentine', u'garda', u"biggie's", u'insecurities\x85', u'gassing', u'stadiums', u'vintage', u'loutishness', u'actionless', u'nwhere', u'adverse', u'flower', u'panning', u'keneth', u'fomentation', u'hopton', u"sousa's", u'tactful', u'booz', u'craigs', u'brumes', u'snatchers', u'appropriated', u'baruc', u'albin', u"'masters", u'antipasto', u'overwork', u'appropriates', u'siouxie', u'outgrow', u'breaker', u'boor', u"'bubbling'", u'rocker', u"arcand's", u'gleason\x85', u"chappelle's", u'wren', u'lowlights', u'storia', u'mighty', u'hmapk', u'perceval', u'downfalls', u'shigeru', u'disturbance', u'serbs', u'ensued', u'sky', u'morbidly', u'googling', u"'intentional'", u'skg', u'adoption', u'timecrimes', u'serbo', u'ski', u'knob', u'saturation', u"andr\xe9's", u'sick', u'refusals', u'protesters', u'kristofferson', u'siemens', u'deviances', u'know', u'knot', u'variants', u'\xc5ge', u'renyolds', u'iberia', u"annabelle's", u'bedfellows', u'praarthana', u'losted', u'starrer', u'dances', u"one''willard'", u'cutlet', u'eviction', u'isolytic', u'starred', u'traitors', u'nigga', u'shabby', u'waqt', u"matrix'", u'junk', u'mosquitoes', u'libretto', u'kattan', u'beguine', u'soninha', u'sequency', u'dubbers', u'zillionaire', u"gettin'", u'zanuck', u'leaded', u'strolls', u'moguls', u'catepillar', u'sette', u'murdoch', u'murdock', u'uneffective', u'poets', u'miniskirts', u'pensaba', u'thoroughfare', u"o'donoghugh", u'arganauts', u'swinginest', u'consuming', u'wesley', u'daud', u'guffaws', u'empahh', u'nationalities', u'throne', u'intercontenital', u'throng', u'getting', u'klineschloss', u'dependence', u'dependency', u'epiphany', u'fudd', u'wonk', u'fitzgibbon', u'bugle', u'unoticeable', u'couldnt', u'gunther', u'cultureless', u"'hall", u"mariner's", u"'time", u'dispossessed', u'deceptiveness', u'patronisingly', u'maize', u'uncontrollably', u'expeditious', u'effeminate', u'hypnotism', u"'half", u'lederer', u'uncontrollable', u'footwork', u"i've", u'proving', u'desando', u'baaaaad', u'gereco', u"bearings'", u'kensit', u'wight', u'contradictory', u'bratwurst', u"may's", u'contradictors', u'amitabhs', u'jaffa', u'jaffe', u'instructive', u"olan's", u'ornella', u'bitva', u'reble', u'percival', u'lubricated', u"matsumoto's", u'heralding', u'hirschbiegel', u"baywatch'", u'odilon', u'meaningless', u'gnawing', u"'solve'", u"guard's", u"yamada's", u'spookfest', u"'transporter'", u'airsoft', u'abhay', u'spanky', u'urrrghhh', u'ev', u'chicatillo', u'transacting', u"'la", u'percent', u'oprah', u'sics', u'illinois', u'dogtown', u'roars', u'branch', u'kerouac', u'wheelers', u'sica', u'lance', u"pipe's", u'discretionary', u'contends', u'copywrite', u'geysers', u'artbox', u'cl\xe9ment', u'cronyn', u'hardboiled', u'lot\xb4s', u'35mm', u"'l'", u'paget', u'expands']
In [15]:
print('Token for "happy"',word_to_id['happy'])
print('Token for "love"',word_to_id['love'])
print('Token for "spiders"',word_to_id['spiders'])
('Token for "happy"', 654)
('Token for "love"', 119)
('Token for "spiders"', 16118)
In [0]:
tokenizer = Tokenizer(num_words=vocab_size, split=' ')
tokenizer.fit_on_texts(word_to_id.keys())
X = tokenizer.texts_to_sequences(word_to_id.keys())
X = pad_sequences(X)
In [17]:
print(X)
[[6] [7] [8] ... [0] [0] [0]]
Build Keras Embedding Layer Model¶
We can think of the Embedding layer as a dicionary that maps a index assigned to a word to a word vector. This layer is very flexible and can be used in a few ways:
- The embedding layer can be used at the start of a larger deep learning model.
- Also we could load pre-train word embeddings into the embedding layer when we create our model.
- Use the embedding layer to train our own word2vec models.
The keras embedding layer doesn't require us to onehot encode our words, instead we have to give each word a unqiue intger number as an id. For the imdb dataset we've loaded this has already been done, but if this wasn't the case we could use sklearn LabelEncoder.
In [19]:
embed_dim = 32
lstm_out = 32
model = Sequential()
model.add(Embedding(vocab_size, embed_dim,input_length = X_train.shape[1]))
model.add(SpatialDropout1D(0.2))
model.add(LSTM(lstm_out, dropout=0.2, recurrent_dropout=0.2))
model.add(Activation('relu'))
model.add(Dense(units=1))
model.add(Activation('sigmoid'))
model.compile(loss = 'binary_crossentropy', optimizer='adam',metrics = ['accuracy'])
print(model.summary())
W0329 09:47:51.803339 140112634922880 module_wrapper.py:139] From /usr/local/lib/python2.7/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead. W0329 09:47:51.819281 140112634922880 module_wrapper.py:139] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:3376: The name tf.log is deprecated. Please use tf.math.log instead. W0329 09:47:51.824014 140112634922880 deprecation.py:323] From /tensorflow-1.15.2/python2.7/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.where in 2.0, which has the same broadcast rule as np.where
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= embedding_2 (Embedding) (None, 300, 32) 320000 _________________________________________________________________ spatial_dropout1d_2 (Spatial (None, 300, 32) 0 _________________________________________________________________ lstm_2 (LSTM) (None, 32) 8320 _________________________________________________________________ activation_2 (Activation) (None, 32) 0 _________________________________________________________________ dense_1 (Dense) (None, 1) 33 _________________________________________________________________ activation_3 (Activation) (None, 1) 0 ================================================================= Total params: 328,353 Trainable params: 328,353 Non-trainable params: 0 _________________________________________________________________ None
In [20]:
batch_size = 32
model.fit(X_train, y_train, epochs = 10, batch_size=batch_size, verbose = 2)
W0329 09:47:57.402246 140112634922880 module_wrapper.py:139] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead. W0329 09:47:57.487437 140112634922880 module_wrapper.py:139] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:973: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead. W0329 09:47:57.537888 140112634922880 module_wrapper.py:139] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:2741: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead. W0329 09:47:57.541960 140112634922880 module_wrapper.py:139] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead. W0329 09:47:57.542670 140112634922880 module_wrapper.py:139] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.
Epoch 1/10
W0329 09:47:57.794357 140112634922880 module_wrapper.py:139] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead. W0329 09:47:57.795715 140112634922880 module_wrapper.py:139] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:199: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead. W0329 09:47:57.982527 140112634922880 module_wrapper.py:139] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:206: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.
- 373s - loss: 0.5060 - acc: 0.7551 Epoch 2/10 - 359s - loss: 0.3533 - acc: 0.8505 Epoch 3/10 - 364s - loss: 0.3155 - acc: 0.8722 Epoch 4/10 - 368s - loss: 0.2849 - acc: 0.8864 Epoch 5/10 - 365s - loss: 0.2700 - acc: 0.8934 Epoch 6/10 - 366s - loss: 0.2245 - acc: 0.9154 Epoch 7/10 - 362s - loss: 0.2093 - acc: 0.9207 Epoch 8/10 - 362s - loss: 0.1933 - acc: 0.9276 Epoch 9/10 - 370s - loss: 0.1800 - acc: 0.9334 Epoch 10/10 - 369s - loss: 0.1724 - acc: 0.9346
Out[20]:
<keras.callbacks.History at 0x7f6e18b2c9d0>
In [21]:
score,acc = model.evaluate(X_test, y_test, verbose = 2, batch_size = batch_size)
print("Score: %.2f" % (score))
print("Accuracy: %.2f" % (acc))
Score: 0.41 Accuracy: 0.85
Graphical representations of the Neural Network
In [23]:
training_history = model.fit(X_train, y_train, epochs= 5, batch_size=batch_size, validation_data=(X_test, y_test))
Train on 25000 samples, validate on 25000 samples Epoch 1/5 25000/25000 [==============================] - 509s 20ms/step - loss: 0.1584 - acc: 0.9419 - val_loss: 0.4877 - val_acc: 0.8199 Epoch 2/5 25000/25000 [==============================] - 517s 21ms/step - loss: 0.1506 - acc: 0.9446 - val_loss: 0.4200 - val_acc: 0.8419 Epoch 3/5 25000/25000 [==============================] - 515s 21ms/step - loss: 0.1405 - acc: 0.9482 - val_loss: 0.4206 - val_acc: 0.8579 Epoch 4/5 25000/25000 [==============================] - 529s 21ms/step - loss: 0.1290 - acc: 0.9523 - val_loss: 0.4483 - val_acc: 0.8582 Epoch 5/5 25000/25000 [==============================] - 530s 21ms/step - loss: 0.1220 - acc: 0.9553 - val_loss: 0.4713 - val_acc: 0.8563
In [25]:
# VALIDATION LOSS curves
plt.clf()
history_dict = training_history.history
loss_values = history_dict['loss']
val_loss_values = history_dict['val_loss']
epochs = range(1, (len(history_dict['loss']) + 1))
plt.plot(epochs, loss_values, 'bo', label='Training loss')
plt.plot(epochs, val_loss_values, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()
In [26]:
# VALIDATION ACCURACY curves
plt.clf()
acc_values = history_dict['acc']
val_acc_values = history_dict['val_acc']
epochs = range(1, (len(history_dict['acc']) + 1))
plt.plot(epochs, acc_values, 'bo', label='Training acc')
plt.plot(epochs, val_acc_values, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.show()
In [27]:
import matplotlib.pyplot as plt
plt.plot(training_history.history['acc'])
plt.plot(training_history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train','test'], loc = 'upper left')
plt.show()
plt.plot(training_history.history['loss'])
plt.plot(training_history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train','test'], loc = 'upper left')
plt.show()
Predition
In [29]:
# Demo on a positive text
my_text = 'I love thriller movies. They are so nice. They are my favourite pastime when I am alone.'
seq = tokenizer.texts_to_sequences([my_text])
print('raw seq:',seq)
seq = pad_sequences(seq, maxlen=maxlen)
print('padded seq:',seq)
prediction = model.predict(seq)
print('positivity:',prediction)
('raw seq:', [[9684, 5481, 9684]])
('padded seq:', array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
9684, 5481, 9684]], dtype=int32))
('positivity:', array([[0.9994246]], dtype=float32))
In [30]:
my_text = 'The bleak economic outlook will force many small businesses into bankruptcy.The future will be dead.'
seq = tokenizer.texts_to_sequences([my_text])
print('raw seq:',seq)
seq = pad_sequences(seq, maxlen=maxlen)
print('padded seq:',seq)
prediction = model.predict(seq)
print('positivity:',prediction)
('raw seq:', [[1996]])
('padded seq:', array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1996]], dtype=int32))
('positivity:', array([[0.6074006]], dtype=float32))
In [0]:
predict=model.predict_classes(X_test)
predict_classes=predict.reshape(len(X_test))
In [0]:
def get_original_text(i):
word_to_id = imdb.get_word_index()
word_to_id = {k:(v+3) for k,v in word_to_id.items()}
word_to_id["<PAD>"] = 0
word_to_id["<START>"] = 1
word_to_id["<UNK>"] = 2
id_to_word = {value:key for key,value in word_to_id.items()}
return ' '.join(id_to_word[id] for id in X_test[i])
In [0]:
SentimentDict={1:'positive', 0:'negative'}
def display_test_sentiment(i):
print(get_original_text(i))
print('label: ', SentimentDict[y_test[i]], ', prediction: ', SentimentDict[predict_classes[i]])
In [41]:
display_test_sentiment(5711)
<PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <START> with david arquette starring you would immediately think this to be a stupid movie well it is a stupid movie with a horrid script but the f x namely the eight <UNK> freaks makes this flick a hoot to watch a tribute albeit on the silly side to those great mutant creature features of the 50s arquette and sheriff sam <UNK> <UNK> <UNK> help in fighting off the toxic waste induced giant <UNK> <UNK> havoc on their tiny town in arizona also in the cast are doug e doug rick leon and the charming scarlett johansson
('label: ', 'negative', ', prediction: ', 'negative')
In [38]:
display_test_sentiment(13000)
<PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <START> this film brought me to tears i have to say that if i did not have a beautiful husband at home i would ask this beautiful piece of art to marry me aaron carter gives a masterful performance as a confused young pop star while timothy barton writes quick and witty dialogue that only <UNK> the genius of <UNK> performance kyle is pretty gay but his performance was nothing less than spectacular he is also very handsome and cute i'm thinking about asking him out on a date and giving him a very sweet <UNK> gift br br if you would like to discuss this film in the future please contact me br br nick <UNK> class of <UNK> <UNK> california <UNK> west
('label: ', 'positive', ', prediction: ', 'positive')
In both the above cases we saw that the model performed really well, model predicted positive in case of actual positive and negative in case of actual negative.
In [42]:
display_test_sentiment(3)
<PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <START> i generally love this type of movie however this time i found myself wanting to kick the screen since i can't do that i will just complain about it this was absolutely idiotic the things that happen with the dead kids are very cool but the alive people are absolute idiots i am a grown man pretty big and i can defend myself well however i would not do half the stuff the little girl does in this movie also the mother in this movie is reckless with her children to the point of neglect i wish i wasn't so angry about her and her actions because i would have otherwise enjoyed the flick what a number she was take my advise and fast forward through everything you see her do until the end also is anyone else getting sick of watching movies that are filmed so dark anymore one can hardly see what is being filmed as an audience we are <UNK> involved with the actions on the screen so then why the hell can't we have night vision
('label: ', 'negative', ', prediction: ', 'positive')
Here we can see that there is a difference in label and prediction, because the model is not 100% accurate.
Retrive the output of each layer in keras for a given single test sample from the trained model you built¶
In [43]:
from keras import backend as K
inp = model.input # input placeholder
outputs = [layer.output for layer in model.layers] # all layer outputs
functor = K.function([inp, K.learning_phase()], outputs ) # evaluation function
# Testing
test = np.random.random(25000)[np.newaxis,...]
layer_outs = functor([test, 1.])
print layer_outs
[array([[[ 0.00443089, -0.06616466, -0.01801574, ..., 0.03174243,
0.09259104, -0.02178793],
[ 0.00443089, -0.06616466, -0.01801574, ..., 0.03174243,
0.09259104, -0.02178793],
[ 0.00443089, -0.06616466, -0.01801574, ..., 0.03174243,
0.09259104, -0.02178793],
...,
[ 0.00443089, -0.06616466, -0.01801574, ..., 0.03174243,
0.09259104, -0.02178793],
[ 0.00443089, -0.06616466, -0.01801574, ..., 0.03174243,
0.09259104, -0.02178793],
[ 0.00443089, -0.06616466, -0.01801574, ..., 0.03174243,
0.09259104, -0.02178793]]], dtype=float32), array([[[ 0.00553862, -0. , -0. , ..., 0.03967804,
0.1157388 , -0.02723491],
[ 0.00553862, -0. , -0. , ..., 0.03967804,
0.1157388 , -0.02723491],
[ 0.00553862, -0. , -0. , ..., 0.03967804,
0.1157388 , -0.02723491],
...,
[ 0.00553862, -0. , -0. , ..., 0.03967804,
0.1157388 , -0.02723491],
[ 0.00553862, -0. , -0. , ..., 0.03967804,
0.1157388 , -0.02723491],
[ 0.00553862, -0. , -0. , ..., 0.03967804,
0.1157388 , -0.02723491]]], dtype=float32), array([[-0.15115128, 0.04613699, 0.44477797, -0.08466609, 0.12647173,
-0.14858477, -0.22761317, -0.20700724, 0.06033586, 0.1318282 ,
-0.04515539, -0.08798737, -0.0955292 , -0.09450727, 0.2747495 ,
-0.08026227, -0.1551854 , -0.16616164, 0.02609047, 0.14396532,
-0.13164113, -0.23721187, -0.10317978, -0.05199071, -0.16391294,
0.11578882, 0.44392037, -0.13544597, -0.15687953, 0.07698756,
0.14485769, -0.21981561]], dtype=float32), array([[0. , 0.04613699, 0.44477797, 0. , 0.12647173,
0. , 0. , 0. , 0.06033586, 0.1318282 ,
0. , 0. , 0. , 0. , 0.2747495 ,
0. , 0. , 0. , 0.02609047, 0.14396532,
0. , 0. , 0. , 0. , 0. ,
0.11578882, 0.44392037, 0. , 0. , 0.07698756,
0.14485769, 0. ]], dtype=float32), array([[1.5281528]], dtype=float32), array([[0.8217359]], dtype=float32)]
In [44]:
# Demo on a positive text
my_text = 'I love thriller movies. They are so nice. They are my favourite pastime when I am alone.'
seq = tokenizer.texts_to_sequences([my_text])
print('raw seq:',seq)
seq = pad_sequences(seq, maxlen=maxlen)
print('padded seq:',seq)
prediction = model.predict(seq)
print('positivity:',prediction)
('raw seq:', [[9684, 5481, 9684]])
('padded seq:', array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
9684, 5481, 9684]], dtype=int32))
('positivity:', array([[0.9994246]], dtype=float32))
In [45]:
print(model.layers[0].output)
print(model.layers[1].output)
print(model.layers[2].output)
print(model.layers[3].output)
print(model.layers[4].output)
print(model.layers[5].output)
Tensor("embedding_2/embedding_lookup/Identity:0", shape=(?, 300, 32), dtype=float32)
Tensor("spatial_dropout1d_2/cond/Merge:0", shape=(?, 300, 32), dtype=float32)
Tensor("lstm_2/TensorArrayReadV3:0", shape=(?, 32), dtype=float32)
Tensor("activation_2/Relu:0", shape=(?, 32), dtype=float32)
Tensor("dense_1/BiasAdd:0", shape=(?, 1), dtype=float32)
Tensor("activation_3/Sigmoid:0", shape=(?, 1), dtype=float32)
In [0]:
def visualize_conv_layer(layer_name):
layer_output=model.get_layer(layer_name).output
intermediate_model= Model(inputs=model.input,outputs=layer_output)
intermediate_prediction=intermediate_model.predict(seq)
row_size=4
col_size=8
img_index=0
print(np.shape(intermediate_prediction))
print(intermediate_prediction)
In [47]:
print(model.layers[0].name)
embedding_2
In [48]:
visualize_conv_layer('embedding_2')
(1, 300, 32)
[[[ 0.00443089 -0.06616466 -0.01801574 ... 0.03174243 0.09259104
-0.02178793]
[ 0.00443089 -0.06616466 -0.01801574 ... 0.03174243 0.09259104
-0.02178793]
[ 0.00443089 -0.06616466 -0.01801574 ... 0.03174243 0.09259104
-0.02178793]
...
[-0.22019203 -0.34096906 0.26238647 ... -0.19100496 0.20718823
0.27028322]
[-0.13725355 -0.04516867 0.06124864 ... -0.14951763 0.13245565
0.15912448]
[-0.22019203 -0.34096906 0.26238647 ... -0.19100496 0.20718823
0.27028322]]]
In [49]:
print(model.layers[1].name)
spatial_dropout1d_2
In [50]:
visualize_conv_layer('spatial_dropout1d_2')
(1, 300, 32)
[[[ 0.00443089 -0.06616466 -0.01801574 ... 0.03174243 0.09259104
-0.02178793]
[ 0.00443089 -0.06616466 -0.01801574 ... 0.03174243 0.09259104
-0.02178793]
[ 0.00443089 -0.06616466 -0.01801574 ... 0.03174243 0.09259104
-0.02178793]
...
[-0.22019203 -0.34096906 0.26238647 ... -0.19100496 0.20718823
0.27028322]
[-0.13725355 -0.04516867 0.06124864 ... -0.14951763 0.13245565
0.15912448]
[-0.22019203 -0.34096906 0.26238647 ... -0.19100496 0.20718823
0.27028322]]]
In [51]:
print(model.layers[2].name)
lstm_2
In [52]:
visualize_conv_layer('lstm_2')
(1, 32) [[-0.0085878 0.3222845 0.9838119 -0.29327103 0.21704283 -0.01320527 0.92685825 -0.396328 0.10598899 0.68732274 -0.5408316 -0.3505058 -0. -0.69003993 0. -0.25924847 -0.58815473 0.93660074 0. -0.6442047 -0. -0. -0.23820026 -0.27366987 0.87868655 0.8171024 0.90759695 -0.12974158 -0.05844785 0. 0. -0.03359791]]
In [53]:
print(model.layers[3].name)
activation_2
In [54]:
visualize_conv_layer('activation_2')
(1, 32) [[0. 0.3222845 0.9838119 0. 0.21704283 0. 0.92685825 0. 0.10598899 0.68732274 0. 0. 0. 0. 0. 0. 0. 0.93660074 0. 0. 0. 0. 0. 0. 0.87868655 0.8171024 0.90759695 0. 0. 0. 0. 0. ]]
In [55]:
print(model.layers[4].name)
dense_1
In [56]:
visualize_conv_layer('dense_1')
(1, 1) [[7.459852]]
In [57]:
print(model.layers[5].name)
activation_3
In [58]:
visualize_conv_layer('activation_3')
(1, 1) [[0.9994246]]
Summary: So, we have built a NLP model that can quite accurately predict the sentiment of a peiece of text and classify it as either positive or negative.